Don'T Skype & Type!: Acoustic Eavesdropping in Voice-Over-IP
Title | Don'T Skype & Type!: Acoustic Eavesdropping in Voice-Over-IP |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Compagno, Alberto, Conti, Mauro, Lain, Daniele, Tsudik, Gene |
Conference Name | Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4944-4 |
Keywords | Human Behavior, human factors, keyboard acoustic eavesdropping, keystroke analysis, machine learning, Metrics, privacy, pubcrawl, security, Side-channel attack, Skype |
Abstract | Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, physical properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models that are not very practical in many real-world settings. Such strong models assume: (i) adversary's physical proximity to the victim, (ii) precise profiling of the victim's typing style and keyboard, and/or (iii) significant amount of victim's typed information (and its corresponding sounds) available to the adversary. This paper presents and explores a new keyboard acoustic eavesdropping attack that involves Voice-over-IP (VoIP), called Skype & Type (S&T), while avoiding prior strong adversary assumptions. This work is motivated by the simple observation that people often engage in secondary activities (including typing) while participating in VoIP calls. As expected, VoIP software acquires and faithfully transmits all sounds, including emanations of pressed keystrokes, which can include passwords and other sensitive information. We show that one very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim's input - keystrokes typed on the remote keyboard. Our results demonstrate that, given some knowledge on the victim's typing style and keyboard model, the attacker attains top-5 accuracy of 91.7% in guessing a random key pressed by the victim. Furthermore, we demonstrate that S&T is robust to various VoIP issues (e.g., Internet bandwidth fluctuations and presence of voice over keystrokes), thus confirming feasibility of this attack. Finally, it applies to other popular VoIP software, such as Google Hangouts. |
URL | https://dl.acm.org/citation.cfm?doid=3052973.3053005 |
DOI | 10.1145/3052973.3053005 |
Citation Key | compagno_dont_2017 |